The present invention relates to anatomical landmark detection in medical image data, and more particularly, to spatially consistent multi-scale deep learning based detection of anatomical landmarks in medical image data.
Fast and robust anatomical object detection is a fundamental task in medical image analysis that supports the entire clinical imaging workflow from diagnosis, patient stratification, therapy planning, intervention, and follow-up. Automatic detection of an anatomical object is a prerequisite for many medical image analysis tasks, such as segmentation, motion tracking, and disease diagnosis and quantification.
Machine learning based techniques have been developed for anatomical landmark detection in medical images. For example, machine learning techniques for quickly identifying anatomy in medical images include Marginal Space Learning (MSL), Marginal Space Deep Learning (MSDL), Marginal Space Deep Regression (MSDR), and Approximated Marginal Space Deep Learning (AMSD). While machine learning techniques are often applied to address the problem of detecting anatomical structures in medical images, the traditional object search scheme used in such techniques is typically driven by suboptimal and exhaustive strategies. Furthermore, these techniques do not effectively address cases of incomplete data, i.e., scans taken with a partial field-of-view. Addressing these limitations of conventional anatomical landmark detection techniques is important to enable artificial intelligence to directly support and increase the efficiency of the clinical workflow from admission through diagnosis, clinical care, and patient follow-up.